Patentable/Patents/US-11405280
US-11405280

AI-driven capacity forecasting and planning for microservices apps

PublishedAugust 2, 2022
Assigneenot available in USPTO data we have
Inventorsnot available in USPTO data we have
Technical Abstract

In one embodiment, a resource allocation process determines a plurality of service levels of applications (e.g., business transactions) during a monitored period, and examines infrastructure performance data (utilization of a plurality of resources and a plurality of performance metrics) of a plurality of services in a microservices architecture in relation to each of the plurality of service levels of the applications. Accordingly, a resource capacity model can be generated for the microservices architecture based on the service dependency and the infrastructure performance data across the plurality of service levels, the resource capacity model defining a required capacity of resources to satisfy specified performance metric constraints during operation of the applications at given service levels. As such, the resource allocation process can effectuate, based on the resource capacity model, a specific capacity of resources required for a particular time of operation of the applications at a particular service level.

Patent Claims
5 claims

Legal claims defining the scope of protection, as filed with the USPTO.

6

6. The method as in claim 1, wherein generating the resource capacity model is based on one or more machine learning techniques selected from a group consisting of: linear regression; curve fitting; and principle component analysis.

9

9. The method as in claim 1, wherein the required capacity of the plurality of resources in order to satisfy the specified performance metric constraints during operation of the plurality of applications comprise both primary and backup resources.

11

11. The method as in claim 1, wherein the one or more specified performance metric constraints on operation of the plurality of applications are based on one or both of one or more service level agreements (SLAs) or one or more policy suite policies.

12

12. The method as in claim 1, wherein the one or more specified performance metric constraints on operation of the plurality of applications are selected from a group consisting of: application response time (ART); application delay; server response time; total response time; and total transaction time.

13

13. The method as in claim 1, wherein the plurality of applications comprise a plurality of business transactions.

Classification Codes (CPC)

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Patent Metadata

Filing Date

July 24, 2019

Publication Date

August 2, 2022

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Cite as: Patentable. “AI-driven capacity forecasting and planning for microservices apps” (US-11405280). https://patentable.app/patents/US-11405280

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